Using the classical CNN network method to construct the automatic extraction model of remote sensing image of Guiyang road elements

doi:10.13474/j.cnki.11-2246.2023.0126

Abstract

Abstract: Road extraction comprehensively consider the accuracy, computing ability and adaptability to the environment of Guiyang in the interpretation process,so several links in the deep learning neural network model are decomposed. Through multiple rounds of comparative experiments and analysis,a model for automatic extraction of remote sensing images of road elements in Guiyang is established in this paper,the data extracted in batches are analyzed and optimized to complete the filling of some road attributes, which largely realizes the automatic intelligent and efficient extraction of road entities.The practical problems and technical routes involved in the process can provide reference for the natural resources business carried out by the municipal and county level satellite remote sensing application technology departments.

Key words: CNN, deep learning, road feature extraction, remote sensing interpretation

CLC Number:

P237

SHE Zuoming, SHEN Yongzhi, SONG Jianhong, XIANG Yujin. Using the classical CNN network method to construct the automatic extraction model of remote sensing image of Guiyang road elements[J]. Bulletin of Surveying and Mapping, 2023, 0(4): 177-182.

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Using the classical CNN network method to construct the automatic extraction model of remote sensing image of Guiyang road elements

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